Using Serverless Framework and AWS to map Near-Realtime Positions of Trains

Background

A couple of months back, I found out about the Open Data initiative from Transport for New South Wales. This is an awesome undertaking, to provide data to developers and other interested parties, so that they can develop great applications. For those interested, the Open Data hub can be accessed at https://opendata.transport.nsw.gov.au.

I had been playing with data for a few months now and when I looked through the various APIs that I could access via the Open Data hub, I became extremely interested.

In this blog, I will take you through one of my mini projects based off the data at Open Data Hub. I will be using the Public Transport – Vehicle Positions API to plot the near realtime positions of Sydney trains on a map. The API provides access to more than just train position data, however to keep things simple, I will concentrate on only trains in this blog.

Solution Architecture

As I am a huge fan of serverless, I decided to architect my solution with as much serverless components as possible. The diagram below shows a high-level architecture of how the Transport Positioning System (this is what I will call my solution, TPS for short) will be created.

Let’s go through the steps (as marked in the diagram above)

The lambda function will query the Open Data API every 5 minutes for the position of all trains

After the data has been received, the lambda function will go through each record and assign a label to each train. To ensure the labels are consistent across each lambda invocation, the train to label association will be stored in an Amazon DynamoDB table. The lambda function will query the table to check if a train has already been allocated a label. If it has, then that label will be used. Otherwise, a new label will be created, and the Amazon DynamoDB table will be updated to store this new train to label association.

I found Bing Maps to be much easier (and cheaper) to use for plotting items on a map. The only disadvantage is that it can, at most show 100 points (called pushpins) on the map. The lambda function will go through the first 100 items returned from Open Data API and using the labels that were found/created in step 2, create a pushpin url that will be used to generate a map of the location of the first 100 trains. This url will then be used to generate the map by sending a request to Bing Maps.

The lambda function will then create a static webpage that displays the map showing the position of the trains, along with a description that provides more information about the labels used for each train (for example, label 1 could have a description of “19:10 Central Station to Penrith Station”). The label description is set to the train’s label, which is obtained from the query results of the Open Data API query.

Above is a screenshot of TPS in action. It is a static webpage that is being generated every 5 minutes, showing the position of trains. I will keep the lambda function running for at least three months, so you have plenty of time available to check it out.

After items 1 – 3 have been completed, create a python runtime Serverless Framework service (my service is called tps)

Within the tps service folder, install the following Serverless Framework plugins

serverless-python-requirements – this plugin adds all the required python modules into a zip file containing our lambda function script, which then gets uploaded to AWS (the required python modules must be defined in the requirement.txt file)

serverless-prune-plugin – this plugin ensures that only the specified number of lambda function versions exist.

Serverless-python-requirements requires a file called requirements.txt. For this solution, create a file called requirements.txt at the root of the service folder and put the following lines inside it

requests==2.22.0
gtfs-realtime-bindings==0.0.6

Register an account with Transport for New South Wales Open Data Hub (https://opendata.transport.nsw.gov.au/). This is free. Once registered, login to the Open Data Hub portal and then under My Account, click on Applications and then create an Application that has permissions to Public Transport – Realtime Vehicle Positions API. Note down the API key that is generated as it will be used by the python script later.

Create an account with Bing Maps, use the Website licence plan. This will provide 125,000 billable transactions of generating maps per calendar year at no charge. For generating a map every 5 minutes, this is more than enough. Note down the API key that is provided. Details on creating a Bing Maps account is available at https://www.microsoft.com/en-us/maps/licensing/options

This project has two parts to it. The first is to create the AWS resources that will host our project. For this, we will be using Serverless Framework to create our AWS Lambda function, Amazon Simple Storage Service (S3) bucket, Amazon DynamoDB table, AWS CloudWatch Logs and AWS CloudWatch Events.

The second part is the API query and data ingestion from the Open Data API. The next sections will cover each of these parts.

AWS Resource Creation

As previously mentioned, we will use Serverless Framework to create our AWS resources. Serverless Framework uses serverless.yml to specify which resources need to be created. This file is created by default whenever a Serverless Framework service is created.

In this section, I will take you through the serverless.yml file I used for this project.

The file starts like this.

It defines the service name, plugins and variables that will be used in this file (notice the plugins serverless-python-requirements and serverless-prune-plugin)

The following default values have been configured in the above serverless.yml file.

application name is set to tps

environment is set to dev

Amazon CloudWatch logs is set to 14 days retention

the AWS region is set to ap-southeast-2 (Sydney)

the Amazon DynamoDB table is set to transportPosition

the billing mode for the Amazon DynamoDB table will be set to PAY PER REQUEST.

The next section defines the details for the cloud provider where resources will be provisioned.

As previously mentioned, I am using AWS. The lambda function will use python 3.7 runtime. The deployment bucket to host all artefacts is also defined. This is an Amazon Simple Storage Service (S3) bucket. Ensure that this S3 bucket exists before deploying the serverless service.

The next section defines the IAM role that will be created for the lambda function.

The specified IAM role will allow the lambda function to carry out all required operations on Amazon Dynamodb table (currently the IAM role permits all DynamoDB actions, however if required, this can be tightened) and to upload objects to the Amazon S3 bucket that will host the static website.

The next section provides instructions on what to include and exclude when creating the serverless package.

Now we come to the important sections for within serverless.yml. The next section defines the lambda function, its handler and what events will trigger it.

For the tps lambda function, the handler function is at src.tps_vehiclepos.run (this means that there is a subfolder within the service folder called src, within which there is a python file called tps_vehiclepos. Inside this file is a function called run). The lambda function will be run every 5 minutes. To achieve this, I am using AWS Events. Two environment variables are also being passed to the lambda function (BUCKET and TRANSPORTPOSITION_TABLE)

The last section defines all the resources that will be created by the Serverless Framework.

The following resources will be created

Amazon S3 bucket. This bucket will store the Bing Maps that show the train positions. It will also serve the website which will be used to display the position maps.

Amazon DynamoDB table. The table will be used to store details for trains found in the Open Data API query. Note that we are also using the DynamoDB TTL feature. Since we don’t need to retain items older than a day, this will allow us to easily prune the Amazon DynamoDB table, to reduce costs.

Generating the map

In this section we will go through the script that does all the magic, which is querying the Open Data API, processing the data, generating the map and then displaying the results in a webpage.

The script is called tps_vehiclelocation.py. I will discuss the script in parts below.

The first section lists the python modules that will be imported. It also defines the variables that will be used within the script.

Remember to replace <insert your opendata api key> and <insert your bing maps api key here> with your own Open Data and Bing Maps api key. Do not include the ‘<‘ ‘>’ in the script.

Next, I will take you through the various functions that have been created to carry out specific tasks.

First up is the initialise global variables function. As you might be aware, AWS Lambdas can get reused over various invocations. During my experimentations, I found this happening and the issue I found was that my global variables were not being automatically initialised. This caused erroneous results. To circumvent this issue, I decided to write an explicit function that will initialise all global variables at the beginning of each lambda function invocation.

When placing each train location on the map (these will be doing using pushpins), a label will be used to identify each pushpin. Unfortunately, Bing Maps doesn’t allow more than three characters for each label. This is not enough to provide a meaningful label. The solution I devised was to use a consecutive numbering scheme for the labels and then provide a key on the website page. The key will provide a description for each label. One complication with this approach is that I need to ensure the same label is used for the same train for any lambda invocation. This is achieved by storing the label to train mapping in the Amazon DynamoDb table.

The next function downloads all label to train associations stored in Amazon DynamoDB. This ensures that labels remain consistent across lambda invocations.

The next function just queries Open Data API for the train positions.

The following function takes the data from the above function and processes it.

The function goes through each vehicle record that was returned and checks to see if the vehicle already has a label associated to it. If there is one, then this label will be used for it otherwise a new one is created. The Amazon DynamoDB table will be updated with this newly created label. This function uses the first 100 trains returned by the Open Data API, to generate a pushpin URL. This URL will be used to generate the Bing Maps showing the positions of the trains.

The following function uses the pushpin URL to create a map using Bing Maps.

The function provides the pushpin URL to Bing Maps. Bing Maps returns a map showing the positions of the trains. The map is then uploaded to the Amazon S3 bucket that will serve the website. The function then goes generates a description page showing descriptions for each label. As you can imagine, each day, there are hundreds of labels being created. Not all of these labels will be displayed on the map, however they would be listed in the key area. To provide quick access to the key descriptions which show trains that are currently displayed in the map, the corresponding entries will be displayed in blue. This ensures that people don’t go on a wild goose chase, trying to locate a train on the map which might not be displayed.

The next function updates the transport position Amazon DynamoDB table with any new labels that were created during this invocation. This ensures that the labels persist for all subsequent lambda invocations.

Now that we know what all the functions do, lets move on to the handler function that the tps lambda will call. The handler function will coordinate all the functions.

The handler function calls the respective functions to get the following tasks done (in the order listed below)

Initialises the global variables

downloads the previously associated labels from the Amazon DynamoDB table

calls Open Data API to get the latest position of the trains.

the train data is then processed, and the pushpin URL generated

Using the pushpin URL, the map is generated using Bing Maps.

A landing page is created. This webpage shows the map along with a key to show descriptions for each label.

Finally, all labels that were created within this lambda invocation are uploaded to the Amazon DynamoDB table. This ensures that for all subsequent invocations of the lambda function, the respective trains get the same label assigned to them.

That’s it! Now you have a good idea about how the AWS resources were generated and how the data was acquired, processed and then visualised.

Cost to run the solution

When I started developing this solution, to view what type of data was being provided by Open Data API, I tried to ingest everything into DynamoDB. This was a VERY VERY bad idea as it cost me quite a lot. However, since then I have modified my code to only ingest and store fields that are required, into Amazon DynamoDB table. This has drastically reduced by running costs. Currently I am being charged approximately USD0.05 or less per day. You can run this easily within your free tier without incurring any additional costs (just as a precaution, monitor your costs to ensure there are no surprises).